A comprehensive survey on age of information in massive IoT networks

Abstract

Ambient intelligence (AmI) represents the future vision of intelligent computing that can bring intelligence to our daily life through various domains. In such applications, AmI is often subject to the freshness of information collected, which is commonly quantified by a relatively newer metric called age of information (AoI). In the data aggregation and analytics for Internet-of-Things (IoT) AmI, AoI should be well managed, because information update should be as timely as possible to achieve optimal performances. AoI has been studied in various applications using different queuing policies, scheduling algorithms, and multiple access schemes, in which each component of communication and information systems are designed and analyzed to improve the AoI. This paper provides a comprehensive overview of literature on the AoI and its variants in large-scale networks. AoI in IoT systems depends on the arrival rate at the source nodes, queuing policy adopted at the nodes, the scheduling of nodes for information transmission and the access scheme adopted by the nodes. To better design and operate the AmI applications that require the freshness of information, we discuss the impacts of the queuing policy, stochastic modeling, scheduling, and multiple access schemes. In particular, non-orthogonal multiple access (NOMA), which is regarded as one of the key technologies in beyond 5G and 6G, and it hybrid version combined with the conventional orthogonal multiple access (OMA) are discussed in the context of AoI. In addition, we identify promising research opportunities in potential age-sensitive applications. Thus, compared to the existing surveys on AoI, this paper provide more practical and up-to-date design guidelines for the applications with the information freshness requirements.